用数据驱动模型加速核反应堆仿真，提速千倍。
Enhancing Nuclear Reactor Core Simulation through Data-Based Surrogate Models
- 构建两类替代仿真模型，融合数据与物理规律。
- 仿真速度提升最高达1000倍，计算开销大幅降低。
- 适合核能系统实时控制与快速决策场景。
近年来，为适应可再生能源的快速发展，核电站需提升运行灵活性。弗拉马托姆公司开发的运行辅助预测系统（OAPS）通过模型预测控制（MPC）应对该挑战。本文旨在通过数据驱动的仿真方案改进MPC方法。基于一组非线性刚性常微分方程（ODEs），提出两种代理模型作为替代仿真方案，以增强核反应堆堆芯仿真能力。结果表明，数据驱动与物理信息融合的模型均可快速整合复杂动力学，计算时间减少高达1000倍。
原文摘要 · Abstract (English)
In recent years, there has been an increasing need for Nuclear Power Plants (NPPs) to improve flexibility in order to match the rapid growth of renewable energies. The Operator Assistance Predictive System (OAPS) developed by Framatome addresses this problem through Model Predictive Control (MPC). In this work, we aim to improve MPC methods through data-driven simulation schemes. Thus, from a set of nonlinear stiff ordinary differential equations (ODEs), this paper introduces two surrogate models acting as alternative simulation schemes to enhance nuclear reactor core simulation. We show that both data-driven and physics-informed models can rapidly integrate complex dynamics, with a very low computational time (up to 1000x time reduction).
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